Dense channel-hybrid PCANet for low-resolution and occluded face recognition
A dense channel-hybrid PCANet(DCH-PCANet)is proposed to recognize low-resolution and occluded face images.Only channel-independent convolutions(CIC)are used in the convolutional layer of the existing principal component analysis network(PCANet)model.Since CIC does not consider the correlation of the feature maps in the channel direction,it can better highlight the local texture features of a single feature map,which is of great sig-nificance for compensating the feature loss caused by low resolution and occlusion.However,CIC will also strengthen the occlusion features,hence enlarging the influence range of bad features.The channel-dependent con-volution(CDC)fully considers the correlation of all feature maps in the channel direction,which can better sup-press the effect of bad features and form a sparse feature map.A CDC-based feature-map extraction branch is added to PCANet to form a channel-hybrid PCANet.And dense connections are also introduced to make full use of low-level features to improve the robustness of occluded image recognition.Experiments are conducted on the following four datasets:AR face dataset,where face images with real occlusions and simulated low-resolutions are acquired in controlled environment;MFR2 and PKU-Masked-Face,where face images with real occlusions and simulated low-resolutions are acquired in uncontrolled environment;our own dataset,where face images with real occlusion and real low-resolution are acquired in uncontrolled environment.Experimental results show that compared with the ex-isting methods,the proposed DCH-PCANet has better occlusion and low-resolution robustness,which can be used as an effective supplement to the cutting-edge methods to improve their recognition performance.
face recognition with occlusionprincipal component analysis network(PCANet)channel-de-pendent convolution(CDC)dense connection
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